




def 					_a							(					lowerCAmelCase = 1000000 )->	Tuple:
		SCREAMING_SNAKE_CASE_					      =							set(range(3    ,						lowercase_    ,						2 ) )
		primes.add(2 )
		for p in range(3    ,						lowercase_    ,						2 ):
				if p not in primes:
						continue
				primes.difference_update(set(range(p * p    ,						lowercase_    ,						lowercase_ ) ) )

		SCREAMING_SNAKE_CASE_					      =							[float(lowercase_ ) for n in range(limit + 1 )]

		for p in primes:
				for n in range(lowercase_    ,						limit + 1    ,						lowercase_ ):
						phi[n] *= 1 - 1 / p

		return int(sum(phi[2:] ) )


if __name__ == "__main__":
						print(f"""{solution() = }""")







'''simple docstring'''







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : List[Any]      			=     ['''image_processor''', '''tokenizer''']
  _a  : List[Any]      			=     '''ViTImageProcessor'''
  _a  : int      			=     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')







  def __init__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	**UpperCamelCase__	):






         """simple docstring"""


         a_						     =			None
         if "feature_extractor" in kwargs:
                warnings.warn(
                    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                    ' instead.'   ,	UpperCamelCase__   ,	)
                a_						     =			kwargs.pop('feature_extractor'	)

         a_						     =			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
                raise ValueError('You need to specify an `image_processor`.'	)
         if tokenizer is None:
                raise ValueError('You need to specify a `tokenizer`.'	)

         super().__init__(UpperCamelCase__   ,	UpperCamelCase__	)







  def __call__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	**UpperCamelCase__	):






         """simple docstring"""


         if text is None and visual_prompt is None and images is None:
                raise ValueError('You have to specify either text, visual prompt or images.'	)

         if text is not None and visual_prompt is not None:
                raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'	)

         if text is not None:
                a_						     =			self.tokenizer(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if visual_prompt is not None:
                a_						     =			self.image_processor(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if images is not None:
                a_						     =			self.image_processor(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if visual_prompt is not None and images is not None:
                a_						     =			{
                    'pixel_values': image_features.pixel_values,
                    'conditional_pixel_values': prompt_features.pixel_values,
                }
                return encoding
         elif text is not None and images is not None:
                a_						     =			image_features.pixel_values
                return encoding
         elif text is not None:
                return encoding
         elif visual_prompt is not None:
                a_						     =			{
                    'conditional_pixel_values': prompt_features.pixel_values,
                }
                return encoding
         else:
                return BatchEncoding(data=dict(**UpperCamelCase__	)   ,	tensor_type=UpperCamelCase__	)







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         return self.tokenizer.batch_decode(*UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         return self.tokenizer.decode(*UpperCamelCase__   ,	**UpperCamelCase__	)







  @property
  def    _a       (			self	):






         """simple docstring"""


         warnings.warn(
             '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,	UpperCamelCase__   ,	)
         return self.image_processor_class







  @property
  def    _a       (			self	):






         """simple docstring"""


         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,	UpperCamelCase__   ,	)
         return self.image_processor




